Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation

• URL: http://arxiv.org/abs/2407.08489v1
• Date: Thu, 11 Jul 2024 13:23:04 GMT
• Title: Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation
• Authors: Zeyang Zhao, Qilong Xue, Yuhang He, Yifan Bai, Xing Wei, Yihong Gong,
• Abstract summary: We introduce the point-axis representation for oriented object detection, emphasizing its flexibility and geometrically intuitive nature with two key components: points and axes. The point-axis representation decouples location and rotation, addressing the loss discontinuity issues commonly encountered in traditional bounding box-based approaches. We present the Oriented DETR model, seamlessly integrating the DETR framework for precise point-axis prediction and end-to-end detection.
• Score: 29.14485159194744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces the point-axis representation for oriented object detection, emphasizing its flexibility and geometrically intuitive nature with two key components: points and axes. 1) Points delineate the spatial extent and contours of objects, providing detailed shape descriptions. 2) Axes define the primary directionalities of objects, providing essential orientation cues crucial for precise detection. The point-axis representation decouples location and rotation, addressing the loss discontinuity issues commonly encountered in traditional bounding box-based approaches. For effective optimization without introducing additional annotations, we propose the max-projection loss to supervise point set learning and the cross-axis loss for robust axis representation learning. Further, leveraging this representation, we present the Oriented DETR model, seamlessly integrating the DETR framework for precise point-axis prediction and end-to-end detection. Experimental results demonstrate significant performance improvements in oriented object detection tasks.

Related papers

• Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking [59.87033229815062]
  Articulated object manipulation requires precise object interaction, where the object's axis must be carefully considered. Previous research employed interactive perception for manipulating articulated objects, but typically, open-loop approaches often suffer from overlooking the interaction dynamics. We present a closed-loop pipeline integrating interactive perception with online axis estimation from segmented 3D point clouds.
  arXiv  Detail & Related papers  (2024-09-24T17:59:56Z)
• GRA: Detecting Oriented Objects through Group-wise Rotating and Attention [64.21917568525764]
  Group-wise Rotating and Attention (GRA) module is proposed to replace the convolution operations in backbone networks for oriented object detection. GRA can adaptively capture fine-grained features of objects with diverse orientations, comprising two key components: Group-wise Rotating and Group-wise Attention. GRA achieves a new state-of-the-art (SOTA) on the DOTA-v2.0 benchmark, while saving the parameters by nearly 50% compared to the previous SOTA method.
  arXiv  Detail & Related papers  (2024-03-17T07:29:32Z)
• PointOBB: Learning Oriented Object Detection via Single Point Supervision [55.88982271340328]
  This paper proposes PointOBB, the first single Point-based OBB generation method, for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view, a resized view, and a rotated/flipped (rot/flp) view. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance.
  arXiv  Detail & Related papers  (2023-11-23T15:51:50Z)
• ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection [55.291579862817656]
  Existing oriented object detection methods commonly use metric AP$_50$ to measure the performance of the model. We argue that AP$_50$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. We propose an Aspect Ratio Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which exhibits a competitive performance.
  arXiv  Detail & Related papers  (2023-03-09T02:20:56Z)
• Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap [16.85421977235311]
  We develop a single-stage rotating object detector via two points with a solar corona heatmap to detect oriented objects. The ROTP predicts parts of the object and then aggregates them to form a whole image.
  arXiv  Detail & Related papers  (2022-02-14T09:07:21Z)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• Oriented RepPoints for Aerial Object Detection [10.818838437018682]
  In this paper, we propose a novel approach to aerial object detection, named Oriented RepPoints. Specifically, we suggest to employ a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box.
  arXiv  Detail & Related papers  (2021-05-24T06:18:23Z)
• Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation [85.96410825961966]
  We argue that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries. To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions. We apply this proposed framework, called Point-Set Anchors, to object detection, instance segmentation, and human pose estimation.
  arXiv  Detail & Related papers  (2020-07-06T15:59:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.